Gene expression profiling predicts clinical outcome of prostate cancer
Gene expression profiling predicts clinical outcome of prostate cancer
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DOI：
10.1172/jci200420032
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发表时间：
2004-03-01
影响因子：
15.9
通讯作者：
Gerald, WL
中科院分区：
文献类型：
--
作者：
Glinsky, GV;Glinskii, AB;Gerald, WL
前列腺癌治疗的主要问题之一是缺乏可靠的遗传标记来预测疾病的临床过程。我们分析了治疗后具有不同临床结果的患者的前列腺肿瘤以及裸鼠转移性人前列腺癌异种移植物中12，625个转录物的表达谱。我们在两个独立的患者队列中，以90%和75%的准确率确定了区分复发性与非复发性疾病的小基因簇。我们检查了一组样本（21个肿瘤），以发现复发预测基因，然后验证了这些基因在另一组（79个肿瘤）中的预测能力。Kaplan-Meier分析表明，复发预测标志在将患者分层为治疗后具有不同无复发生存率的亚组时具有高度信息性（P < 0.0001）。基于基因表达的复发预测算法在预测术前前列腺特异性抗原水平高或低的早期疾病患者的结局方面提供了信息，并为基于Gleason总和或多参数列线图的结局预测提供了额外的价值。总体而言，88%的前列腺癌复发患者在治疗后1年内被正确归类为预后不良组。所确定的算法提供了额外的预测价值超过传统的标志物的结果，似乎适合分层的前列腺癌患者在诊断时，治疗后不同的生存概率的亚组。
One of the major problems in management of prostate cancer is the lack of reliable genetic markers predicting the clinical course of the disease. We analyzed expression profiles of 12,625 transcripts in prostate tumors from patients with distinct clinical outcomes after therapy as well as metastatic human prostate cancer xenografts in nude mice. We identified small clusters of genes discriminating recurrent versus nonrecurrent disease with 90% and 75% accuracy in two independent cohorts of patients. We examined one group of samples (21 tumors) to discover the recurrence predictor genes and then validated the predictive power of these genes in a different set (79 tumors). Kaplan-Meier analysis demonstrated that recurrence predictor signatures are highly informative (P < 0.0001) in stratification of patients into subgroups with distinct relapse-free survival after therapy. A gene expression-based recurrence predictor algorithm was informative in predicting the outcome in patients with early-stage disease, with either high or low preoperative prostate-specific antigen levels and provided additional value to the outcome prediction based on Gleason sum or multiparameter nomogram. Overall, 88% of patients with recurrence of prostate cancer within 1 year after therapy were correctly classified into the poor-prognosis group. The identified algorithm provides additional predictive value over conventional markers of outcome and appears suitable for stratification of prostate cancer patients at the time of diagnosis into subgroups with distinct survival probability after therapy.